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The objectives of this research are to analyze the sociodemographic and personality profiles most related to good academic time management among 845 students from different faculties at the University de Barcelona (UB) and to identify the explanatory factors of effective academic time management.

Poor time management is a common behavior among university students and an explanatory factor for academic failure. A sociodemographic questionnaire, the Procrastination Assessment Scale-Student (PASS), the Academic Time Management (ATM), the Brief Symptom Inventory (BSI-18) and the Big Five Inventory-10 (BFI-10) were administered.

The results reveal that female students, education majors and those with high academic performance show better time management than the rest of the student body. Additionally, students who have better academic time management are also more neurotic, more open to experience, more responsible and less prone to procrastination. The factors established as explanatory of good academic time management are neuroticism, openness to experience and low procrastination.

The implications of the results for promoting academic time management in university studies through specific actions are discussed.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

Time-cost trade-off is normal conduct in construction projects when projects are expectedly late for delivery. Existing research on time-cost trade-off strategic management mostly focused on the technical calculation towards the optimal combination of activities to be accelerated, while the managerial aspects are mostly neglected. This paper aims to understand the managerial efforts necessary to prepare construction projects ready for an upcoming trade-off implementation.

A preliminary list of critical factors was first identified from the literature and verified by a Delphi survey. Quantitative data was then collected by a questionnaire survey to first shortlist the preliminary factors and quantify the predictive model with different machine learning algorithms, i.e. k-nearest neighbours (kNN), radial basis function (RBF), multiplayer perceptron (MLP), multinomial logistic regression (MLR), naïve Bayes classifier (NBC) and Bayesian belief networks (BBNs).

The model's independent variable importance ranking revealed that the top challenges faced were the realism of contractual obligation, contractor planning and control and client management and monitoring. Among the tested machine learning algorithms, multilayer perceptron was demonstrated to be the most suitable in this case. This model accuracy reached 96.5% with the training dataset and 95.6% with an independent test dataset and could be used as the contingency approach for time-cost trade-offs.

The identified factor list contributed to the theoretical explanation of the failed implementation in general and practical managerial improvement to better avoid such failure. In addition, the established predictive model provided an ad-hoc early warning and diagnostic tool to better ensure time-cost implementation success.

The present research aims to investigate how lean project management (LPM) and agile project management (APM) affect project performance outcomes in the construction sector in Jordan. This study focuses on six key project performance outcomes, namely cost, time, quality, client satisfaction, innovation and responsiveness.

The present study employed a quantitative approach to achieve the research objectives. Accordingly, a multi-item survey questionnaire was prepared to gather data from 392 project managers from construction companies in Jordan. The study’s model showed acceptable levels regarding reliability, validity, fit indices and discriminant validity. In order to test the hypotheses of this study, path analysis was employed using Amos 24.0 software.

LPM demonstrated a remarkably high positive impact on cost performance. It also positively affected quality performance and client satisfaction. However, LPM insignificantly affected time, innovation and responsiveness performance measures. On the other hand, APM showed a notably high positive impact on innovation and responsiveness. The findings also revealed that APM positively impacted quality performance and client satisfaction. In addition, APM negatively impacted cost performance and insignificantly impacted time performance.

This study is one of the first comprehensive studies to empirically examine the impact of both LPM and APM on various project performance outcomes in the construction industry in the context of a developing country. It reveals some similarities and differences between LPM and APM with regard to their impacts on project management outcomes. The findings are expected to guide managers in selecting the appropriate project management approach based on the desired performance outcomes. Accordingly, it offers important implications for project managers in construction companies.

The recent pandemic period (COVID-19), while negatively impacting many companies, has contributed to the growth and adoption of online platforms such as marketplaces and e-commerce. This environment has led many companies, which previously acted only through offline channels, to adopt new technologies and online channels and develop new e-commerce strategies. Small and micro enterprises are most vulnerable due to their limited resources and lack of capabilities. For this reason, the main objective of this paper is to unveil the e-commerce implementation capabilities that micro and small enterprises should build and the challenges they must face when managing an e-commerce strategy.

The authors adopted an inductive qualitative research design approach focused on multiple case studies. The firms operate in the food and beverage industry.

The findings identify several e-commerce implementation capabilities that micro and small enterprises operating in the food and beverage industry should build to manage e-commerce strategies. These are related to outsourcing management, multichannel management, time management, internal stock management and marketplace choice. Moreover, the paper identifies key e-commerce implementation challenges these firms must cope with. These regard distribution management, potential loss of control, fresh product management and lack of resources, time and capabilities.

This research shows that proper capacity management in the implementation of micro and small enterprises e-commerce strategies is critical to achieving efficient results and preventing challenges that threaten such strategies. The research offers guidelines and frameworks for micro and small enterprises to understand how to manage e-commerce and face its challenges.

This study aims to investigate a unique approach to learning languages through self-directed online learning. Specifically, it explores the self-management abilities and skills learners need while learning a language outside traditional classroom settings when using mobile-assisted learning technology.

A mixed-methods approach was used in this study, including an online survey of 84 people and 10 semi-structured interviews.

Findings reveal the significant role of specific and well-defined learning goals in enhancing learners’ performance. These goals can be either self-initiated by the learners themselves or defined by the technological features of the learning platform. However, the presence of distractions in learners’ daily lives presents challenges to effective time management, affecting learners both physically and psychologically. A key aspect of self-directed language learning lies in the learners’ ability to seek out relevant human and material resources beyond the confines of a single mobile-assisted language learning (MALL) tool. The authenticity of these resources is crucial in ensuring meaningful and effective learning experiences.

Understanding how learners navigate and discover valuable resources is a central focus of this study. This research offers valuable insights into the field of self-directed language learning, revealing the pivotal role of self-management skills with mobile-assisted learning technology. The findings contribute to the broader field of language education and offer practical implications for educators and developers seeking to optimize self-directed language learning experiences through innovative and technologically driven approaches.

MALL is often ideal for individualized informal learning, but the existing literature focuses heavily on formal learning situations, underestimating the importance of MALL practices in various informal settings. Most research reports on MALL-based self-directed learning primarily sample traditional English-learning university students. Therefore, there is a need for research on how nontraditional older adult learners self-direct their language learning with mobile technology outside the classroom.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

The advent of Industry 4.0 (I4.0) and the requirements imposed on companies still need to be clarified. Companies still strive to understand I4.0 requirements and technological, organizational, operational and management challenges. Current literature on I4.0 underlies the importance of a roadmap with structured steps to achieve the benefits of I4.0, mainly focused on augmenting operational performance. Therefore, this paper proposes a roadmap to implement I4.0 focused on operational management concepts, mainly aiming to augment operational performance and bridge the gap between theory and practice regarding roadmaps focused on the operational management dimension.

This paper follows a research approach divided into the following stages: a literature review to analyze the I4.0 roadmaps and identify the main components of I4.0; development of the proposed I4.0 roadmap presented; field research to test the roadmap by collecting data from a manufacturing company in the automotive industry; validation of the roadmap through modeling and simulation.

The authors presented a production line design with real-time control, fast response, shop floor coordination and predictive capacity. The results prove that the proposed I4.0 roadmap augments operation performance in the investigated automotive company. The main results were work in process reduction, lead time reduction, output increase, real-time control, shop floor coordination and fast response.

The main novelty of the proposed roadmap is to move toward I4.0 implementation with a focus on the operational management dimension. The roadmap has an innovative combination of the two approaches – lean manufacturing and factory physics – a straightforward roadmap with only three steps: (1) requirements, (2) real-time control and (3) predictive capacity, a structured definition of the approaches and operational management concepts fundamental in each step.

The inherent risks and their interactive impacts in megaproject development have been found in numerous cases worldwide. Although risk management standards have been recommended for the best practice in engineering construction projects, there is still a lack of systematic approaches to describing the interactions. Interactions such as social, technical, economic, ecological and political (STEEP) risks have complex and dynamic implications for megaproject construction. For a better understanding and effective management of megaprojects such as the Edinburgh Tram project, the dynamic interaction of concomitant risks must be studied.

A systems dynamic methodology was adopted following the comprehensive literature review. Documentary data were gathered from the case study on Tram Network Project in Edinburgh.

A casual loop of typical evolution of key indicators of risks was then developed. A hypothesised model of social and ecological (SE) risks was derived using the system dynamics (SD) modelling technique. The model was set up following British Standards on risk management to provide a generic tool for risk management in megaproject development. The study reveals that cost and time overruns at the developmental stage of the case project are caused mainly by the effects of interactions of risk factors from the external macro project environment on a timely basis.

This article presented a model for simulating the socio-ecological risk confronting the management and construction of megaprojects. The use of SD provided the opportunity to explain the nature of all risks, particularly the SE risks in the past stages of project development.

Construction material management is an important process in supporting construction operations that affect project performance. Previous studies attempt to identify factors influencing material management in different stages such as procurement, transportation and utilization. However, they lack a model to explain the relationship between influential factors and the effectiveness of material management. Therefore, this study aims to validate the variables and key factors influencing the effectiveness of material management processes.

A total of 42 variables were reviewed from literature in different stages of material management process. Survey questionnaires were used to collect data about participants' perceptions on these variables. The respondents were 200 project engineers and project managers from construction sites and offices in Vietnam. Then factor analysis techniques were undertaken to validate the structure of factor groups. Two methods of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were, respectively, performed to evaluate and verify the model's fit.

Factors influencing the effectiveness of material management were grouped into nine main factors, which are procurement issues, site conditions, planning and handling on site, industrial environments, contractual issues, quality control, suppliers and manufacturers' issues, transportation in and out site and security on site.

The paper has several implications for theory and methodology related to material management. It features influential factors in association with the material management effectiveness. Therefore, senior managers can more fully understand the errors in their works and propose timely solutions to limit the unwanted risks.

This research contributes on theoretical development on factors influencing effectiveness of material management processes. The key findings on influential factors can be applied to measure the effectiveness of material management processes.